Evaluating electronic submissions using generative artificial intelligence

Abstract

Aspects of the present disclosure relate to automated evaluation of electronic datasets. Embodiments include receiving one or more rules related to evaluation of electronic datasets. Embodiments further include generating, via an embedding model, embedding representations of the one or more rules. Embodiments further include receiving an electronic dataset. Embodiments further include identifying a rule that is applicable to the electronic dataset based on using a machine learning model configured to search the embedding representations of the one or more rules based on the electronic dataset. Embodiments further include evaluating, using the machine learning model or an additional machine learning model, the electronic dataset based on the identified rule. Embodiments further include using the machine learning model or the additional machine learning model to generate an evaluation summary for the electronic dataset based on determining that an item within the electronic dataset does not comply with the identified rule.

What is claimed is: 
     
         1 . A method of automatic electronic dataset evaluation, comprising:
 receiving one or more rules related to evaluation of electronic datasets;   generating, via an embedding model, embedding representations of the one or more rules;   receiving an electronic dataset;   identifying a rule that is applicable to the electronic dataset based on using a machine learning model configured to search the embedding representations of the one or more rules based on the electronic dataset; and   evaluating, using the machine learning model or an additional machine learning model, the electronic dataset based on the identified rule.   
     
     
         2 . The method of  claim 1 , wherein the evaluating comprises determining that an item within the electronic dataset does not comply with the identified rule. 
     
     
         3 . The method of  claim 2 , further comprising using the machine learning model or the additional machine learning model to generate an evaluation summary for the electronic dataset based on the determining. 
     
     
         4 . The method of  claim 3 , further comprising receiving user feedback based on the evaluation summary, wherein the user feedback is used to retrain the machine learning model or the additional machine learning model. 
     
     
         5 . The method of  claim 3 , further comprising receiving user feedback based on the evaluation summary, wherein an embedding representation of the user feedback is generated by the embedding model. 
     
     
         6 . The method of  claim 3 , wherein the machine learning model or the additional machine learning model comprises a language processing machine learning model, and wherein the evaluation summary comprises natural language instructions for correcting the electronic dataset. 
     
     
         7 . The method of  claim 1 , wherein the searching of the embedding representations of the one or more rules based on the electronic dataset is based on a token within the electronic dataset. 
     
     
         8 . The method of  claim 1 , wherein the electronic dataset comprises a markup language file. 
     
     
         9 . The method of  claim 1 , wherein the embedding model is trained through a supervised learning process involving evaluating training entities. 
     
     
         10 . The method of  claim 1 , wherein the machine learning model is trained through a supervised learning process involving evaluating training entities. 
     
     
         11 . The method of  claim 1 , further comprising storing the embedding representations of the one or more rules in a vector store, wherein the searching of the embedding representations of the one or more rules based on the electronic dataset comprises searching the vector store. 
     
     
         12 . A system for automatic electronic dataset evaluation, comprising:
 one or more processors; and   a memory comprising instructions that, when executed by the one or more processors, cause the system to:
 receive one or more rules related to evaluation of electronic datasets; 
 generate, via an embedding model, embedding representations of the one or more rules; 
 receive an electronic dataset; 
 identify a rule that is applicable to the electronic dataset based on using a machine learning model configured to search the embedding representations of the one or more rules based on the electronic dataset; and 
 evaluate, using the machine learning model or an additional machine learning model, the electronic dataset based on the identified rule. 
   
     
     
         13 . The system of  claim 12 , wherein the evaluating comprises determining that an item within the electronic dataset does not comply with the identified rule. 
     
     
         14 . The system of  claim 13 , wherein the instructions further cause the system to use the machine learning model or the additional machine learning model to generate an evaluation summary for the electronic dataset based on the determining. 
     
     
         15 . The system of  claim 14 , wherein the instructions further cause the system to receive user feedback based on the evaluation summary, wherein the user feedback is used to retrain the machine learning model or the additional machine learning model. 
     
     
         16 . The system of  claim 14 , wherein the machine learning model or the additional machine learning model comprises a language processing machine learning model, and wherein the evaluation summary comprises natural language instructions for correcting the electronic dataset. 
     
     
         17 . The system of  claim 12 , wherein the searching of the embedding representations of the one or more rules based on the electronic dataset is based on a token within the electronic dataset. 
     
     
         18 . The system of  claim 12 , wherein the embedding model is trained through a supervised learning process involving evaluating training entities. 
     
     
         19 . The system of  claim 12 , wherein the machine learning model is trained through a supervised learning process involving evaluating training entities. 
     
     
         20 . The system of  claim 12 , wherein the instructions further cause the system to store the embedding representations of the one or more rules in a vector store, wherein the searching of the embedding representations of the one or more rules based on the electronic dataset comprises searching the vector store.